Title: Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing Show Chinese title

Title: 多模态深度引导滤波用于可理解的医学图像处理

Abstract: Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a "blackbox" transformation. In medical imaging, this lack of comprehensibility of the results is a sensitive issue. The integration of known operators into the deep learning environment has proven to be advantageous for the comprehensibility and reliability of the computations. Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. The output images are only processed by the guided filter while the guidance map can be trained to be task-optimal in an end-to-end fashion. We investigate the performance based on two popular tasks: image super resolution and denoising. The evaluation is conducted based on pairs of multi-modal magnetic resonance imaging and cross-modal computed tomography and magnetic resonance imaging datasets. For both tasks, the proposed approach is on par with state-of-the-art approaches. Additionally, we can show that the input image's content is almost unchanged after the processing which is not the case for conventional deep learning approaches. On top, the proposed pipeline offers increased robustness against degraded input as well as adversarial attacks. Show Chinese abstract

Abstract: 基于深度学习的图像处理能够生成高度吸引人的结果。 然而，它仍然被广泛视为一种“黑箱”转换。 在医学成像中，结果的不可解释性是一个敏感问题。 将已知算子集成到深度学习环境中已被证明对计算的可解释性和可靠性有益。 因此，我们提出结合学习得到的引导图使用局部线性引导滤波器用于通用的医学图像处理。 输出图像仅由引导滤波器处理，而引导图可以在端到端的方式下被训练为任务最优。 我们基于两个流行的任务来研究性能：图像超分辨率和去噪。 评估是基于多模态磁共振成像和跨模态计算机断层扫描与磁共振成像数据集进行的。 对于这两个任务，所提出的方法与最先进的方法相当。 此外，我们可以证明处理后的输入图像内容几乎未发生变化，这与传统深度学习方法不同。 此外，所提出的流程在面对退化输入以及对抗攻击时具有更高的鲁棒性。